Precision machine vision inspection systems (or “vision systems” for short) can be utilized to obtain precise dimensional measurements of inspected objects and to inspect various other object characteristics. Such systems may include a computer, a camera and optical system, and a precision stage that is movable in multiple directions to allow workpiece inspection. One exemplary prior art system, that can be characterized as a general-purpose “off-line” precision vision system is the commercially available QUICK VISION® series of PC-based vision systems and QVPAK® software available from Mitutoyo America Corporation (MAC), located in Aurora, Ill. The features and operation of the QUICK VISION® series of vision systems and the QVPAK® software are generally described, for example, in the QVPAK 3D CNC Vision Measuring Machine User's Guide, published January 2003, and the QVPAK 3D CNC Vision Measuring Machine Operation Guide, published September 1996, each of which is hereby incorporated by reference in their entirety. This type of system is able to use a microscope-type optical system and move the stage so as to provide inspection images of either small or relatively large workpieces at various magnifications.
Machine vision inspection systems generally utilize automated video inspection. U.S. Pat. No. 6,542,180 (the '180 patent) teaches various aspects of such automated video inspection and is incorporated herein by reference in its entirety. As taught in the '180 patent, automated video inspection metrology instruments generally have a programming capability that allows an automatic inspection event sequence to be defined by the user for each particular workpiece configuration. This can be implemented by text-based programming, for example, or through a recording mode which progressively “learns” the inspection event sequence by storing a sequence of machine control instructions corresponding to a sequence of inspection operations performed by a user with the aid of a graphical user interface, or through a combination of both methods. Such a recording mode is often referred to as “learn mode” or “training mode.” Once the inspection event sequence is defined in “learn mode,” such a sequence can then be used to automatically acquire (and additionally analyze or inspect) images of a workpiece during “run mode.”
The machine control instructions including the specific inspection event sequence (i.e., how to acquire each image and how to analyze/inspect each acquired image) are generally stored as a “part program” or “workpiece program” that is specific to the particular workpiece configuration. For example, a part program defines how to acquire each image, such as how to position the camera relative to the workpiece, at what lighting level, at what magnification level, etc. Further, the part program defines how to analyze/inspect an acquired image, for example, by using one or more video tools such as edge/boundary detection video tools.
Video tools (or “tools” for short) and other graphical user interface features may be used manually to accomplish manual inspection and/or machine control operations (in “manual mode”). Their set-up parameters and operation can also be recorded during learn mode, in order to create automatic inspection programs, or “part programs.” Video tools may include, for example, edge/boundary detection tools, autofocus tools, shape or pattern matching tools, dimension measuring tools, and the like.
Measurement accuracy and repeatability in the micron or submicron range are routinely obtained along the X and Y axes (that is, axes parallel to the plane of the inspection images used by precision machine vision inspection systems), in particular when the features being measured are within a single field of view. However, the level of accuracy for the measurement of the distance between features that are separated by more than one field of view tends to be lower than that for features within a single field of view. In particular, when features are being measured that are separated by more than one field of view, the overall distance measurement between the features is typically determined by the difference in the stage positions between the respective images that contain the features to be analyzed, plus the difference in the feature positions in the respective images. The stage positions are typically determined by position encoders (e.g., scale-based encoders), for which the potential position errors may be greater than what is desired for certain applications and/or displacements. It would be desirable for a machine vision inspection system to operate with improved accuracy for measuring distances between features that are separated by more than one field of view.